Papers with Llama3-8B
A Perspective on LLM Data Generation with Few-shot Examples: from Intent to Kubernetes Manifest (2025.acl-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have transformed how complex tasks can be automated . traditional cloud computing operations involve complex manual configurations . |
| Approach: | They propose a pipeline for generating K8s manifests directly from user-described intents expressed in natural language using LLMs. |
| Outcome: | The proposed pipeline can generate K8s manifests directly from user-described intents expressed in natural language using LLMs. |
Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Using dynamic slicing, large language models can be used to reduce computational burden and improve performance. |
| Approach: | They propose a dynamic layer-specific pruning approach that leverages the newly proposed Layer Redundancy score to prune parts of individual layers based on redundancy. |
| Outcome: | The proposed method maintains and enhances model performance over the SliceGPT baseline. |
Global Eye: Breaking the “Fixed Thinking Pattern” during the Instruction Expansion Process (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods focus on constructing multi-perspective prompts to expand instructions, overlooking the “Fixed Thinking Pattern” issue of Large Language Models. |
| Approach: | They propose a method that analyzes the statistical characteristics of newly generated instructions and updates the prompts after a fixed number of instruction expansions. |
| Outcome: | The proposed method surpasses open-source LLMs and GPT3.5 in several metrics. |
Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits (2025.findings-acl)
Copied to clipboard
Khushboo Singh, Vasudha Varadarajan, Adithya V Ganesan, August Håkan Nilsson, Nikita Soni, Syeda Mahwish, Pranav Chitale, Ryan L. Boyd, Lyle Ungar, Richard N Rosenthal, H. Schwartz
| Challenge: | Large Language Models (LLMs) are increasingly used in human-centered applications, yet their ability to model diverse psychological constructs is not well understood. |
| Approach: | They evaluated a range of Transformer-LMs to predict psychological variables across five major dimensions: affect, substance use, mental health, sociodemographics, and personality. |
| Outcome: | The models predict affect, substance use, mental health, sociodemographics, and personality across five major dimensions. |